Intended to be a growing resource of actionable issue briefs, this series is being developed and disseminated by the Academy to further the discussion around important topics that arise in data science. Although each issue brief stands alone, together they provide a more complete, contextual view of an emerging area of focus. |
![]() |
This issue brief defines discrimination (including distinguishing between discrimination, unfair discrimination, and unjust discrimination); presents practical methods for testing and monitoring algorithms; provides a regulatory overview of the issue; and identifies considerations for actuaries, algorithm creators, and regulators.
This issue brief examines the key types of data bias that actuaries may encounter and focuses on the kinds of biases found in modeling data and the implications for algorithmic outcomes.